08c Machine Learning: Multidimensional Scaling
Lecture on multidimensional scaling for feature projection. Reduce the dimensionality while preserving the dissimilarity between the training samples. Follow along with the demonstration workflow in Python's scikit-learn package: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/ma
From playlist Machine Learning
This clip gives describes a rotation matrix in 2D. The clip is from the book "Immersive Linear Algebra" available at http://www.immersivemath.com.
From playlist Chapter 6 - The Matrix
21c Spatial Data Analytics: Scaling Statistics
Subsurface modeling course lecture on scaling statistics.
From playlist Spatial Data Analytics and Modeling
How to multiply complex numbers
How to multiply complex numbers. Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Complex Numbers
Multidimensional Scaling - An EXTREMELY POWERFUL algorithm
Entry to #some2 Multidimensional scaling is also similar to PCA and has other names as well. I hope this video informs you of the basics. You can probably analyse a clean dataset using it now if you know some R or Python. Top 1000 Instagram Influencer dataset: https://www.kaggle.com/d
From playlist Summer of Math Exposition 2 videos
BM8.3. Mappings 3: Composition and Inverse Mappings
Basic Methods: We define composition of mappings and draw parallels to multiplication of real numbers. Items include associativity, identity, and commutativity. Consideration of multiplicative inverses leads to the definition of an inverse mapping, and we give conditions for its existenc
From playlist Math Major Basics
Woojin Kim (6/15/20): Spatiotemporal persistent homology for dynamic metric spaces
Title: Spatiotemporal persistent homology for dynamic metric spaces Abstract: Characterizing the dynamics of time-evolving data within the framework of topological data analysis (TDA) has been attracting increasingly more attention. Popular instances of time-evolving data include flocking
From playlist ATMCS/AATRN 2020
Reliability 3: Cronbach's alpha and internal reliability
In this video, I discuss Cronbach's alpha and internal reliability. I also demonstrate how to compute Cronbach's alpha in SPSS and make sense of SPSS output such as corrected-item-total correlation, squared multiple correlations, and Cronbach's alpha if item deleted.
From playlist Reliability analysis
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
Example 2: Multiplication Involving Mixed Numbers
This video provides two more examples of multiplying mixed numbers. Complete video list at http://www.mathispower4u.com
From playlist Multiplying and Dividing Mixed Numbers
R - Item Response Theory Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers Item Factor Analysis and Item Response Theory from the Beaujean SEM in R book. IRT information also pulled from StatsCamp materials taught by William Skorupski (highly recommend his class!). Both dic
From playlist Structural Equation Modeling
Naotoshi Nakamura - LAVENDER extracts individual variability...
Naotoshi Nakamura - LAVENDER extracts individual variability in the response to seasonal influenza vaccination The human immune system is known to be highly variable among individuals, but it is not well understood how the variability changes over time, especially when faced with externa
From playlist From Molecules and Cells to Human Health : Ideas and concepts
Multidimensional spectroscopy with quantum light and in optical cavities by Shaul Mukamel
Open Quantum Systems DATE: 17 July 2017 to 04 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore There have been major recent breakthroughs, both experimental and theoretical, in the field of Open Quantum Systems. The aim of this program is to bring together leaders in the Open Q
From playlist Open Quantum Systems
Nikos Sidiropoulos: "Supervised Learning and Canonical Decomposition of Multivariate Functions"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Supervised Learning and Canonical Decomposition of Multivariate Functions (Joint work with Nikos Kargas)" Nikos Sidiropoulos - Uni
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Multiplicaton of Polynomials (L3.4)
This video provides several examples of how to multiply polynomials. This video provides examples of how to solve geometry problems using polynomials.
From playlist Multiplying Polynomials
From playlist CS50 Sections 2012